As well known, with the spread of digital devices such as portable multimedia players (PMP) and DivX players and Internet environments such as WiBro (wireless broadband) and FTTH (Fiber To The Home), digitalized music, dramas, and movies has become easily played back, and promptly transferred and shared.
Furthermore, infringement of copyrights due to illegal transfer and sharing of digital contents having copyrights has also been increased. In particular, damage to video contents is being increased due to a fall in prices of memories and increase in data transfer rate.
In such environments, demand for video filtering systems based on video identification is increasing to protect video contents. A video filtering system generally extracts unique features of an original video requiring protection of copyright, and stores the extracted features in a database. If it is requested to transfer and share video contents, the video filtering system extracts features of an object video and compares the extracted features with those of the original video stored in the database in order to determine whether or not the video filtering system filters the object video using the comparison result.
In particular, it is very important to extract features of a video despite that the video is subject to compression, conversion in size, and conversion in frame rate during transfer and sharing of the video to perform video filtering.
Meanwhile, conventional technologies for extracting and identifying features of a video have been suggested in the art.
A conventional technology is suggested by Job Oosteven et al, “Feature Extraction and a Database Strategy for Video Fingerprinting” (Proceeding of International Conference on Recent Advances in Visual Information Systems, 2002). This paper presents a method for the identification of video. The method includes calculating average luminance of image blocks and extracting features of images using differences between the average luminance in temporal and spatial domain to identify the videos.
Another conventional technology is suggested by in Sunil Lee and Chang D. Yoo, “VIDEO FINGERPRINTING BASED ON CENTROIDS OF GRADIENT ORIENTATIONS,” Proc. ICASSP 2006, vol 2., pp. 401-404. Lee's method includes dividing an input video into blocks, calculating centroids of gradient orientations of the blocks using luminance of the blocks and performing a fingerprinting matching based on the centroids of gradient orientations.
However, the Oosteven's method has an advantage of search efficiency, but a drawback in that identification ratio is relatively low with respect to variation in luminance and uniformization of histogram. Further, the Lee's method is effective in respect to a variety of compression techniques and change in size, but requires a large amount of calculations since centroids of gradient orientations with respect to all pixels needs to be calculated.